app.py

import os
import openai
import langchain
import nltk
import gradio as gr
import shutil
import tempfile
from datasets import load_dataset

from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import TokenTextSplitter
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.document_loaders import UnstructuredFileLoader

openai.api_key = os.environ.get("OPENAI_API_KEY")

disclaimer = """
注意事項及免責事項 Disclaimer and Precautions.
"""

# Configure HuggingFace repository
repo_path = "your-username/your-repo-name"
persist_directory = f"hf://{repo_path}/data/"

# Function for processing uploaded file
def process_uploaded_file(file):
    if file is not None:
        with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
            tmp_file.write(file.read())
            tmp_file.flush()
            
            dataset = load_dataset("text", data_files=tmp_file.name, split="train")
            dataset.save_to_disk(persist_directory)

        loader = UnstructuredFileLoader(persist_directory)
        uploaded_doc = loader.load()

        text_splitter = TokenTextSplitter(chunk_size=100, chunk_overlap=30)
        uploaded_docs = text_splitter.split_documents(uploaded_doc)

        embeddings = OpenAIEmbeddings()
        vStore = Chroma.from_documents(uploaded_docs, embeddings)

        global model
        model = RetrievalQA.from_chain_type(
            llm=ChatOpenAI(temperature=0.5, model_name="gpt-3.5-turbo", max_tokens=256),
            chain_type="stuff",
            retriever=vStore.as_retriever()
        )

# Define the function
def askandanswer(question, language, uploaded_file):
    process_uploaded_file(uploaded_file)
    return model.run("请创建一个简单的回答" + language + "问题。 [问题] " + question)

# Create a web application
app = gr.Interface(
    fn=askandanswer,
    inputs=[
        gr.Textbox(placeholder="请输入查询"),
        gr.Dropdown(["中文 Chinese", "英语 English"], label="语言 Language"),
        gr.UploadButton()
    ],
    outputs="text",
    title="文件的聊天知音",
    description="这是一个可以和任何文件进行理解的助手",
    article=disclaimer
)

# Launch the web app
app.launch()